Can this skill help detect DGA (Domain Generation Algorithms)?

Yes, it includes specialized workflows to analyze DNS queries for high-entropy patterns typical of DGAs and identifies potential DNS tunneling attempts.

Does it support encrypted traffic analysis?

It provides methods for TLS analysis, including SNI extraction and JA3 fingerprinting, to identify malicious servers even within encrypted HTTPS streams without needing full decryption.

Can I use this for live incident response?

While optimized for PCAP analysis from sandbox environments, the methods and signature generation logic are highly effective for identifying live malicious activity during active incident response.

Which tools are required for this malware analysis skill?

This skill utilizes industry-standard tools like Wireshark/tshark, Zeek, Suricata, and Python libraries such as Scapy for deep packet inspection and automated analysis.

Malware Network Traffic Analysis

Name: Malware Network Traffic Analysis
Author: mukul975

bymukul975

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4,121

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Security & Testing

Analyzes network traffic from malware to identify command-and-control protocols, data exfiltration channels, and malicious behavior patterns.

This skill empowers AI agents to perform deep packet analysis on malware captures (PCAPs). It provides structured workflows for identifying C2 communication structures, detecting DNS tunneling, analyzing beaconing patterns with statistical jitter analysis, and generating production-ready Suricata or Snort signatures. It is an essential capability for security researchers and incident responders who need to understand how malware interacts with external infrastructure and automate the creation of network-based indicators of compromise.

Key Features

01Automated beaconing detection and periodic communication analysis

02Comprehensive PCAP analysis using tshark and Wireshark statistics

03TLS fingerprinting using JA3/JA3S hashes and SNI extraction

04DNS activity detection including DGA and tunneling identification

054,121 GitHub stars

06Custom Suricata and Snort signature generation from observed traffic

Use Cases

01Reverse engineering proprietary malware C2 protocols from packet captures

02Generating network-based IOCs immediately following malware sandbox execution

03Detecting covert data exfiltration through non-standard channels like DNS or ICMP

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add mukul975/anthropic-cybersecurity-skills analyzing-network-traffic-of-malware

For use in Claude.ai and ChatGPT

Key Features

01Automated beaconing detection and periodic communication analysis

02Comprehensive PCAP analysis using tshark and Wireshark statistics

03TLS fingerprinting using JA3/JA3S hashes and SNI extraction

04DNS activity detection including DGA and tunneling identification

054,121 GitHub stars

06Custom Suricata and Snort signature generation from observed traffic

Use Cases

01Reverse engineering proprietary malware C2 protocols from packet captures

02Generating network-based IOCs immediately following malware sandbox execution

03Detecting covert data exfiltration through non-standard channels like DNS or ICMP

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add mukul975/anthropic-cybersecurity-skills analyzing-network-traffic-of-malware

For use in Claude.ai and ChatGPT